import numpy as np

class Neuron():
    def __init__(self, weights, bias):
        self.weights = weights
        self.bias = bias
        
    def feedforward(self, inputs):
        # weight inputs, add bias, then use the activation function
        total = np.dot(self.weights, inputs) + self.bias
        return sigmoid(total)
    
def sigmoid(x):
    # Sigmoid activation function: f(x) = 1 / (1 + e^(-x))
    return 1 / (1 + np.exp(-x))

def deriv_sigmoid(x):
    # Derivative of sigmoid: f'(x) = f(x) * (1 - f(x))
    fx = sigmoid(x)
    return fx * (1 - fx)

def mse_loss(y_true, y_pred):
    # y_true and y_pred are numpy arrays of the same length
    return ((y_true - y_pred) ** 2).mean()

class OurNeuralNetwork():
    """
    A neural network with:
      - 2 inputs
      - a hidden layer with 2 neurons (h1, h2)
      - an output layer with 1 neuron (o1)
      
    *** DISCLAIMER ***
    The code below is intend to be simple and educational, NOT optimal.
    Real neural net code looks nothing like this. Do NOT use this code.
    Instead, read/run it to understand how this specific network works.
    """
    def __init__(self):
        # weights
        self.w1 = np.random.normal()
        self.w2 = np.random.normal()
        self.w3 = np.random.normal()
        self.w4 = np.random.normal()
        self.w5 = np.random.normal()
        self.w6 = np.random.normal()
        # biases
        self.b1 = np.random.normal()
        self.b2 = np.random.normal()
        self.b3 = np.random.normal()
        
    def feedforward(self, x):
        # x is a numpy array with 2 elements, for example [input1, input2]
        h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)
        h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)
        o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)
        return o1
    
    def train(self, data, all_y_trues):
        """
        - data is a (n x 2) numpy array, n = # samples in the dataset.
        - all_y_trues is a numpy array with n elements.
        Elements in all_y_trues correspond to those in data.
        """
        learn_rate = 0.1
        epochs = 1000 # number of times to loop through the entire dataset
        
        for epoch in range(epochs):
            for x, y_true in zip(data, all_y_trues):
                # Do a feedforward (we'll need these values later)
                sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1
                h1 = sigmoid(sum_h1)
                
                sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2
                h2 = sigmoid(sum_h2)
                
                sum_o1 = self.w5 * x[0] + self.w6 * x[1] + self.b3
                o1 = sigmoid(sum_o1)
                y_pred = o1
                
                # - - - Calculate partial derivatives.
                # - - - Naming: d_L_d_w1 represents "partial L / partial w1"
                d_L_d_ypred = -2 * (y_true - y_pred)
                
                # Neuron o1
                d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)
                d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)
                d_ypred_d_b3 = deriv_sigmoid(sum_o1)
                
                d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)
                d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)
                
                # Neuron h1
                d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)
                d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)
                d_h1_d_b1 = deriv_sigmoid(sum_h1)
                
                # Neuron h2
                d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)
                d_h2_d_w4 = x[0] * deriv_sigmoid(sum_h2)
                d_h2_d_b2 = deriv_sigmoid(sum_h2)
                
                # - - - update weights and biases
                # Neuron h1
                self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1
                self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2
                self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1
                
                # Neuron h2
                self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3
                self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4
                self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2
                
                # Neuron o1
                self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5
                self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6
                self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3
                
            # - - - Calculate total loss at the end of each epoch
            if epoch % 10 == 0:
                y_preds = np.apply_along_axis(self.feedforward, 1, data)
                loss = mse_loss(all_y_trues, y_preds)
                print("Epoch %d loss: %.3f", (epoch, loss))
                
# Define dataset
data = np.array([
    [-2, -1], # Alice
    [25, 6],  # Bob
    [17, 4],  # Charlie
    [-15, -6] # diana
])
all_y_trues = np.array([
    1, # Alice
    0, # Bob
    0, # Charlie
    1 # diana
])

# Train our neural network!
network = OurNeuralNetwork()
network.train(data, all_y_trues)

# Make some predictions
emily = np.array([-7, -3]) # 128 pounds, 63 inches
frank = np.array([20, 2])  # 155 pounds, 68 inches
print("Emily: %.3f" % network.feedforward(emily)) # 0.951 - F
print("Frank: %.3f" % network.feedforward(frank)) # 0.039 - M

